Unverified50% confidenceFactExact time
Research shows that the level of confidence in model-generated text has no significant correlation with its accuracy.
1
Sources
50%
Confidence
Long-term
Relevance
9/17/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedPerplexity measures a language model's ability to predict test text; the lower the value, the less surprised the model is by the text.75% similarUnverifiedText generated by large language models tends to have low perplexity, with word choices that are overly consistent with probabilistic expectations, lacking the unexpectedness of human writing.70% similarUnverifiedIn the experiments, plain text input achieved higher accuracy than input augmented with syntactic and rhetorical information.68% similarUnverifiedThe detection reliability of deeply rewritten content, non-English text, and short text is generally lower.66% similarVerifiedTypical characteristics of text output by mainstream large language models include well-structured sentence patterns, clear paragraph organization, frequent use of emphatic vocabulary, and explicit logical chains — a phenomenon researchers refer to as "stylistic bias."65% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/930423API
curl https://kongchang.com/api/v1/knowledge/claims/930423MCP
get_claim(id=930423)